I think we're talking on a different level here. I'm rather thinking about the performance from an ops perspective. If you write highly specific code that runs

I think we're talking on a different level here. I'm rather thinking about the performance from an ops perspective. If you write highly specific code that runs close to the processor, of course predictability improves. But if you write a web application, possibly running in a virtualised environment, using remote databases and other network services, good look predicting performance to the end user. Even talking about tight loops, you simply can't predict if it will go and stay in the processor cache in layered, multi-tenant situations like that. There is little correlation between theory and practice of a complex system when it comes to performance and reliability. For example, big-O notation tells you very little about the myriads of wonderful ways systems and networks can crash and burn. Disks never failed in my university classes. The more you know about the difference between a perfect theoretical system and the harsh randomness of a real one, the foresight of experience, the better a programmer you are, and this is not generally knowledge that comes from formal education. In short, to be a better programmer, you need to know the difference between the best case ideal scenario and what is actually going to happen, usually when you least expect it...